Free Course
Intro to Machine Learning
School Of Artificial Intelligence
This class will teach you the end-to-end process of investigating data through a machine learning lens, and you'll apply what you've learned to a real-world data set.
Sample Nanodegree Programs
- AI Product Manager
- Machine Learning Engineer
- AI Programming with Python
This Course
Free Course
Intro to Machine Learning
Industry-relevant content
Project reviews
Mentorship
Certification
Career Services / Job Assistance
Related Nanodegree
Nanodegree Program
Introduction to Machine Learning
Industry-relevant content
Project reviews
Mentorship
Certification
Career Services / Job Assistance
Course Details
Course Details Free
Timeline Approx. 10 Weeks
Skill Level Intermediate
Included in Product
Rich Learning Content
Taught by Industry Professionals
Student Support Community
Interactive Quizzes
Self-Paced Learning
Intro to Machine Learning
Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.
Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.
This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.
This course is also a part of our Data Analyst Nanodegree.
Course Leads
Katie Malone
Instructor
Sebastian Thrun
InstructorSyllabus
lesson 1
Welcome to Machine Learning
- Learn what Machine Learning is and meet Sebastian Thrun!
- Find out where Machine Learning is applied in Technology and Science.
lesson 2
Naive Bayes
- Use Naive Bayes with scikit learn in python.
- Splitting data between training sets and testing sets with scikit learn.
- Calculate the posterior probability and the prior probability of simple distributions.
lesson 3
Support Vector Machines
- Learn the simple intuition behind Support Vector Machines.
- Implement an SVM classifier in SKLearn/scikit-learn.
- Identify how to choose the right kernel for your SVM and learn about RBF and Linear Kernels.
lesson 4
Decision Trees
- Code your own decision tree in python.
- Learn the formulas for entropy and information gain and how to calculate them.
- Implement a mini project where you identify the authors in a body of emails using a decision tree in Python.
lesson 5
Choose your own Algorithm
- Decide how to pick the right Machine Learning Algorithm among K-Means, Adaboost, and Decision Trees.
lesson 6
Datasets and Questions
- Apply your Machine Learning knowledge by looking for patterns in the Enron Email Dataset.
- You'll be investigating one of the biggest frauds in American history!
lesson 7
Regressions
- Understand how continuous supervised learning is different from discrete learning.
- Code a Linear Regression in Python with scikit-learn.
- Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.
lesson 8
Outliers
- Remove outliers to improve the quality of your linear regression predictions.
- Apply your learning in a mini project where you remove the residuals on a real dataset and reimplement your regressor.
- Apply your same understanding of outliers and residuals on the Enron Email Corpus.
lesson 9
Clustering
- Identify the difference between Unsupervised Learning and Supervised Learning.
- Implement K-Means in Python and Scikit Learn to find the center of clusters.
- Apply your knowledge on the Enron Finance Data to find clusters in a real dataset.
lesson 10
Feature Scaling
- Understand how to preprocess data with feature scaling to improve your algorithms.
- Use a min mx scaler in sklearn.